Inferring Human-Robot Performance Objectives During Locomotion Using Inverse Reinforcement Learning and Inverse Optimal Control

Quantitatively characterizing a locomotion performance objective for a human-robot system is an important consideration in the assistive wearable robot design towards human-robot symbiosis. This problem, however, has only been addressed sparsely in the literature. In this study, we propose a new inv...

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Vydané v:IEEE robotics and automation letters Ročník 7; číslo 2; s. 2549 - 2556
Hlavní autori: Liu, Wentao, Zhong, Junmin, Wu, Ruofan, Fylstra, Bretta L, Si, Jennie, Huang, He Helen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Cost function
Experiments
Human performance
Inverse problems
Knee
Learning
Learning from demonstration
Legged locomotion
Locomotion
Optimal control
Prostheses
Prosthetics
Quadratic forms
reinforcement learning
Robot control
Robot dynamics
Robots
Stability analysis
Symbiosis
Visualization
Walking
wearable robotics
Wearable robots
Wearable technology
ISSN:2377-3766, 2377-3766
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Popis
Shrnutí:Quantitatively characterizing a locomotion performance objective for a human-robot system is an important consideration in the assistive wearable robot design towards human-robot symbiosis. This problem, however, has only been addressed sparsely in the literature. In this study, we propose a new inverse approach from observed human-robot walking behavior to infer a human-robot collective performance objective represented in a quadratic form. By an innovative design of human experiments and simulation study, respectively, we validated the effectiveness of two solution approaches to solving the inverse problem using inverse reinforcement learning (IRL) and inverse optimal control (IOC). The IRL-based experiments of human walking with robotic transfemoral prosthesis validated the realistic applicability of the proposed inverse approach, while the IOC-based analysis provided important human-robot system properties such as stability and robustness that are difficult to obtain from human experiments. This study introduces a new tool to the field of wearable lower limb robots. It is expected to be expandable to quantify joint human-robot locomotion performance objectives for personalizing wearable robot control in the future.
Bibliografia:ObjectType-Article-1
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3143579